Microscopy Image Dataset for Deep Learning-Based Quantitative Assessment of Pulmonary Vascular Changes.

Pulmonary hypertension (PH) is a syndrome complex that accompanies a number of diseases of different etiologies, associated with basic mechanisms of structural and functional changes of the pulmonary circulation vessels and revealed pressure increasing in the pulmonary artery. The structural changes in the pulmonary circulation vessels are the main limiting factor determining the prognosis of patients with PH. Thickening and irreversible deposition of collagen in the pulmonary artery branches walls leads to rapid disease progression and a therapy effectiveness decreasing. In this regard, histological examination of the pulmonary circulation vessels is critical both in preclinical studies and clinical practice. However, measurements of quantitative parameters such as the average vessel outer diameter, the vessel walls area, and the hypertrophy index claimed significant time investment and the requirement for specialist training to analyze micrographs. A dataset of pulmonary circulation vessels for pathology assessment using semantic segmentation techniques based on deep-learning is presented in this work. 609 original microphotographs of vessels, numerical data from experts' measurements, and microphotographs with outlines of these measurements for each of the vessels are presented. Furthermore, here we cite an example of a deep learning pipeline using the U-Net semantic segmentation model to extract vascular regions. The presented database will be useful for the development of new software solutions for the analysis of histological micrograph.

Reversal in the drought stress response of the Scots pine forest ecosystem: Local soil water regime as a key to improving climate change resilience.

In a changing climate, forest ecosystems have become increasingly vulnerable to continuously exacerbating heat and associated drought conditions. Climate stress resilience is governed by a complex interplay of global, regional, and local factors, with hydrological conditions being among the key players. We studied a Scots pine (Pinus sylvestris L.) forest ecosystem located near the southern edge of the boreal ecotone, which is particularly subjected to frequent and prolonged droughts. By comparing the dendrochronological series of pines growing in apparently contrasting hydrological conditions ranging from the waterlogged peat bog area to the dry soil at the surrounding elevations, we investigated how the soil water regime affects the climate response and drought stress resilience of the forest ecosystem. We found that in the dry land area, a significant fraction of the trees were replaced after two major climate extremes: prolonged drought and extremely low winter temperatures. The latter has also been followed by a three- to ten-fold growth reduction of the trees that survived in the next year, whereas no similar effect has been observed in the peat bog area. Multi-scale detrended partial cross-correlation analysis (DPCCA) indicated that tree-ring width (TRW) was negatively correlated with spring and summer temperatures and positively correlated with the Palmer drought severity index (PDSI) for the same year. For the elevated dry land area, the above effect extends to interannual scales, indicating that prolonged heatwaves and associated droughts are among the factors that limit tree growth. In marked contrast, in the waterlogged peat bog area, a reversed tendency was observed, with prolonged dry periods as well as warmer springs and summers over several consecutive years, leading to increasing tree growth with a one- to three-year time lag. Altogether, our results indicate that the pessimal conditions of a warming climate could become favorable through the preservation of the soil water regime.

Brightfield vs Fluorescent Staining Dataset-A Test Bed Image Set for Machine Learning based Virtual Staining.

Differential fluorescent staining is an effective tool widely adopted for the visualization, segmentation and quantification of cells and cellular substructures as a part of standard microscopic imaging protocols. Incompatibility of staining agents with viable cells represents major and often inevitable limitations to its applicability in live experiments, requiring extraction of samples at different stages of experiment increasing laboratory costs. Accordingly, development of computerized image analysis methodology capable of segmentation and quantification of cells and cellular substructures from plain monochromatic images obtained by light microscopy without help of any physical markup techniques is of considerable interest. The enclosed set contains human colon adenocarcinoma Caco-2 cells microscopic images obtained under various imaging conditions with different viable vs non-viable cells fractions. Each field of view is provided in a three-fold representation, including phase-contrast microscopy and two differential fluorescent microscopy images with specific markup of viable and non-viable cells, respectively, produced using two different staining schemes, representing a prominent test bed for the validation of image analysis methods.